Abstract

Title: SurvivEHR: a competing risks, time-to-event foundation model for multiple long-term conditions from primary care electronic health records

Abstract: Multiple long-term conditions (MLTCs) or multimorbidity – the co-occurrence of multiple chronic conditions –presents a growing challenge for primary care. Current predictive models often target single outcomes and overlook the complexities of time-to-event risk in real-world, longitudinal health data. Here, we present SurvivEHR, a generative transformer-based foundation model trained on over 7.6 billion coded events from 23 million patients in UK primary care. SurvivEHR introduces a competing risk time-to-event pretraining objective that enables accurate forecasting of future diagnoses, investigations, medications, and mortality. We demonstrate that SurvivEHR achieves strong risk stratification performance, captures clinically meaningful trajectories, and outperforms benchmark survival models across multiple tasks. The model also transfers effectively to fine-tuned prognostic tasks, particularly in low-resource settings. By learning patient trajectories directly from routine health records, SurvivEHR offers a scalable and privacy-preserving approach for building generalisable clinical risk tools that address the complexity of MLT Cs in primary care.

Bio: Christopher Yau is Professor of Artificial Intelligence and a UKRI Turing AI Fellow at the University of Oxford in the Nuffield Department for Women’s and Reproductive Health where he co-directs the Ellison Institute of Technology Centre for Doctoral Training in Fundamentals of AI. He is also Co-Programme Lead for Capacity Building at Health Data Research UK where he leads the Wellcome PhD Programme in Health Data Science.